Energy consumption has recently been widely recognized as a major challenge of computer systems design. This paper explores how to support energy as a first-class operating system resource. Energy, because of its global system nature, presents challenges beyond those of conventional resource management. To meet these challenges we propose the Currentcy Model that unifies energy accounting over diverse hardware components and enables fair allocation of available energy among applications. Our particular goal is to extend battery lifetime by limiting the average discharge rate and to share this limited resource among competing tasks according to user preferences. To demonstrate how our framework supports explicit control over the battery resource we implemented ECOSystem, a modified Linux, that incorporates our currentcy model. Experimental results show that ECOSystem accurately accounts for the energy consumed by asynchronous device operation, can achieve a target battery lifetime, and proportionally shares the limited energy resource among competing tasks.

present s GRACE-OS, an energy-e#cient soft real-t-e CPU scheduler for mobile devicestvi primarily run multD]A1D applicatD4D[ The major goal of GR CE-OS is t support applicatDD qualit y of service and save energy. To achievet his goal, GR CE-OS int egrat] dynamic volt]% scaling int osoft real-t1D scheduling and decides howfast t execut applicat37: inaddit]] t when and how longt executt hem. GR CE-OS makes such scheduling decisions based ont heprobabilit y distD[A1%M3 of applicat%M cycle demands, andobt23] t he demanddistA3:772A via online profiling and est]:A1%]2 We have implementl GR CE-OS in tA Linux kernel and evaluat: it on an HPlapt7 wit a variable-speed CPU and mult37MA1 codecs. Our experiment alresult showt hat (1) tA demand dist2DA1%%M oft he stA7M7 codecs isst373 or changes smoot73 . ThisstsA74[ y impliest hat it is feasiblet o perform st chast: scheduling and volt]3 scaling wit low overhead; (2) GR CE-OS deliverssoft performance guarant ees by boundingtn deadline miss rat4 under applicat7:]7M ecific requirementu and (3) GR CE-OS reduces CPU idlet ime and spends more busyt ime in lower-power speeds. Our measurement indicati t hat comparedt o det::2A1744M scheduling and volt age scaling, GR CE-OS saves energy by 7% t 72% while delivering stMD%A17%D performance guarant ees.

This paper presents the design and implementation of a compiler algorithm that effectively optimizes programs for energy usage using dynamic voltage scaling (DVS). The algorithm identifies program regions where the CPU can be slowed down with negligible performance loss. It is implemented as a source-to-source level transformation using the SUIF2 compiler infrastructure. Physical measurements on a high-performance laptop show that total system (i.e., laptop) energy savings of up to 28% can be achieved with performance degradation of less than 5% for the SPECfp95 benchmarks. On average, the system energy and energydelay product are reduced by 11% and 9%, respectively, with a performance slowdown of 2%. It was also discovered that the energy usage of the programs using our DVS algorithm is within 6% from the theoretical lower bound. To the best of our knowledge, this is one of the first work that evaluates DVS algorithms by physical measurements.

Combining high performance with low power consumption is becoming one of the primary objectives of processor designs. Instead of relying just on sleep mode for conserving power, an increasing number of processors take advantage of the fact that reducing the clock frequency and corresponding operating voltage of the CPU can yield quadratic decrease in energy use. However, performance reduction can only be beneficial if it is done transparently, without causing the software to miss its deadlines. In this paper, we describe the implementation and performance-setting algorithms used in

Scalability of the core frequency is a common feature of low-power processor architectures. Many heuristics for frequency scaling were proposed in the past to find the best trade-off between energy efficiency and computational performance. With complex applications exhibiting unpredictable behavior these heuristics cannot reliably adjust the operation point of the hardware because they do not know where the energy is spent and why the performance is lost. Embedded hardware monitors in the form of event counters have proven to offer valuable information in the field of performance analysis. We will demonstrate that counter values can also reveal the power-specific characteristics of a thread. In this paper we propose an energy-aware scheduling policy for non-real-time operating systems that benefits from event counters. By exploiting the information from these counters, the scheduler determines the appropriate clock frequency for each individual thread running in a time-sharing environment. A recurrent analysis of the thread-specific energy and performance profile allows an adjustment of the frequency to the behavioral changes of the application. While the clock frequency may vary in a wide range, the application performance should only suffer slightly. Because of the similarity to a car cruise control, we called our scheduling policy Process Cruise Control. This adaptive clock scaling is accomplished by the operating system without any application support. Process Cruise Control has been implemented on the Intel XScale architecture, that offers a variety of frequencies and a set of configurable event counters. Energy measurements of the target architecture under variable load show the advantage of the proposed approach.

Power management has become increasingly necessary in large-scale datacenters to address costs and limitations in cooling or power delivery. This paper explores how to integrate power management mechanisms and policies with the virtualization technologies being actively deployed in these environments. The goals of the proposed VirtualPower approach to online power management are (i) to support the isolated and independent operation assumed by guest virtual machines (VMs) running on virtualized platforms and (ii) to make it possible to control and globally coordinate the effects of the diverse power management policies applied by these VMs to virtualized resources. To attain these goals, VirtualPower extends to guest VMs ‘soft ’ versions of the hardware power states for which their policies are designed. The resulting technical challenge is to appropriately map VM-level updates made to soft power states to actual changes in the states or in the allocation of underlying virtualized hardware. An implementation of VirtualPower Management (VPM) for the Xen hypervisor addresses this challenge by provision of multiple system-level abstractions including VPM states, channels, mechanisms, and rules. Experimental evaluations on modern multicore platforms highlight resulting improvements in online power management capabilities, including minimization of power consumption with little or no performance penalties and the ability to throttle power consumption while still meeting application requirements. Finally, coordination of online methods for server consolidation with VPM management techniques in heterogeneous server systems is shown to provide up to 34% improvements in power consumption.

The high power consumption of modern processors becomes a major concern because it leads to decreased mission duration (for battery-operated systems), increased heat dissipation, and decreased reliability. While many techniques have been proposed to reduce power consumption for uniprocessor systems, there has been considerably less work on multiprocessor systems. In this paper, based on the concept of slack sharing among processors, we propose two novel power-aware scheduling algorithms for task sets with and without precedence constraints executing on multiprocessor systems. These scheduling techniques reclaim the time unused by a task to reduce the execution speed of future tasks and, thus, reduce the total energy consumption of the system. We also study the effect of discrete voltage/speed levels on the energy savings for multiprocessor systems and propose a new scheme of slack reservation to incorporate voltage/speed adjustment overhead in the scheduling algorithms. Simulation and trace-based results indicate that our algorithms achieve substantial energy savings on systems with variable voltage processors. Moreover, processors with a few discrete voltage/speed levels obtain nearly the same energy savings as processors with continuous voltage/speed, and the effect of voltage/speed adjustment overhead on the energy savings is relatively small.

In this paper, we address power-aware scheduling of periodic tasks to reduce CPU energy consumption in hard real-time systems through dynamic voltage scaling. Our intertask voltage scheduling solution includes three components: (a) a static (o#-line) solution to compute the optimal speed, assuming worst-case workload for each arrival, (b) an online speed reduction mechanism to reclaim energy by adapting to the actual workload, and (c) an on-line, adaptive and speculative speed adjustment mechanism to anticipate early completions of future executions by using the average-case workload information. All these solutions still guarantee that all deadlines are met. Our simulation results show that our reclaiming algorithm alone outperforms other recently proposed inter-task voltage scheduling schemes. Our speculative techniques are shown to provide additional gains, approaching the theoretical lower-bound by a margin of 10%.

Dynamic voltage scaling (DVS) is a promising method for embedded systems to exploit multiple voltage and frequency levels and to prolong battery life. However, pure DVS techniques do not perform well for systems with dynamic workloads where the job execution times vary significantly. In this paper, we present a novel approach combining feedback control with DVS schemes targeting hard real-time systems with dynamic workloads. Our method relies strictly on operating system support by integrating a DVS scheduler and a feedback controller within the EDF scheduling algorithm. Each task is divided into two portions. Within the first portion, the objective is to exploit frequency scaling for the average execution time. We reserve enough time for the second portion to meet the deadline requirements up to the worst-case execution time following a last-chance approach. Feedback techniques make the system capable to select the right frequency and voltage settings for the first potion, as well as guaranteeing hard real-time requirements for the overall task. Simulation experiments demonstrate the ability of our algorithm to save up to 29 % more energy than previous work for task sets with different dynamic workload characteristics. 1.

Power management in data centers has become an increasingly important concern. Large server installations are designed to handle peak load, which may be significantly larger than in off-peak conditions. The increasing cost of energy consumption and cooling incurred in farms of highperformance web servers make low-power operation during off-peak hours desirable. This paper investigates adaptive algorithms for dynamic voltage scaling in QoS-enabled web servers to minimize energy consumption subject to service delay constraints. We implement these algorithms inside the Linux kernel. The instrumented kernel supports multiple client classes with per-class deadlines. Energy consumption is minimized by using a feedback loop that regulates frequency and voltage levels to keep the synthetic utilization around the aperiodic schedulability bound derived in an earlier publication. Enforcing the bound ensures that deadlines are met. Our evaluation of an Apache server running on the modified Linux kernel shows that non-trivial offpeak energy savings are possible without sacrificing timeliness.